






class 							__UpperCamelCase     :







	def __init__(	self   :				List[Any]							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				List[Any]      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	name
		__lowercase   							=	val







	def __str__(	self   :				Union[str, Any]      )		->			Union[str, Any]:
		"""simple docstring"""







		return F'{self.__class__.__name__}({self.name}, {self.val})'







	def __lt__(	self   :				Optional[int]							,					_lowerCAmelCase   :				int      )		->			Any:
		"""simple docstring"""







		return self.val < other.val


class 							__UpperCamelCase     :







	def __init__(	self   :				Union[str, Any]							,					_lowerCAmelCase   :				Dict      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	{}
		__lowercase   							=	{}
		__lowercase   							=	self.build_heap(_lowerCAmelCase      )







	def __getitem__(	self   :				List[Any]							,					_lowerCAmelCase   :				int      )		->			Union[str, Any]:
		"""simple docstring"""







		return self.get_value(_lowerCAmelCase      )







	def 			_a (	self   :				str							,					_lowerCAmelCase   :				Tuple      )		->			Union[str, Any]:
		"""simple docstring"""







		return (idx - 1) // 2







	def 			_a (	self   :				Optional[Any]							,					_lowerCAmelCase   :				Optional[int]      )		->			int:
		"""simple docstring"""







		return idx * 2 + 1







	def 			_a (	self   :				List[Any]							,					_lowerCAmelCase   :				Optional[int]      )		->			Optional[Any]:
		"""simple docstring"""







		return idx * 2 + 2







	def 			_a (	self   :				int							,					_lowerCAmelCase   :				Optional[Any]      )		->			str:
		"""simple docstring"""







		return self.heap_dict[key]







	def 			_a (	self   :				Optional[Any]							,					_lowerCAmelCase   :				Any      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	len(_lowerCAmelCase      ) - 1
		__lowercase   							=	self.get_parent_idx(_lowerCAmelCase      )

		for idx, i in enumerate(_lowerCAmelCase      ):
			__lowercase   							=	idx
			__lowercase   							=	i.val

		for i in range(_lowerCAmelCase							,					-1							,					-1      ):
			self.sift_down(_lowerCAmelCase							,					_lowerCAmelCase      )
		return array







	def 			_a (	self   :				List[str]							,					_lowerCAmelCase   :				List[Any]							,					_lowerCAmelCase   :				Tuple      )		->			List[str]:
		"""simple docstring"""







		while True:
			__lowercase   							=	self.get_left_child_idx(_lowerCAmelCase      )  # noqa: E741
			__lowercase   							=	self.get_right_child_idx(_lowerCAmelCase      )

			__lowercase   							=	idx
			if l < len(_lowerCAmelCase      ) and array[l] < array[idx]:
				__lowercase   							=	l
			if r < len(_lowerCAmelCase      ) and array[r] < array[smallest]:
				__lowercase   							=	r

			if smallest != idx:
				__lowercase							,    __lowercase   							=	array[smallest], array[idx]
				(
				    (
				    __lowercase
				)							,    (
				    __lowercase
				)							,    
				)   							=	(
				    self.idx_of_element[array[smallest]],
				    self.idx_of_element[array[idx]],
				)
				__lowercase   							=	smallest
			else:
				break







	def 			_a (	self   :				int							,					_lowerCAmelCase   :				Dict      )		->			List[str]:
		"""simple docstring"""







		__lowercase   							=	self.get_parent_idx(_lowerCAmelCase      )
		while p >= 0 and self.heap[p] > self.heap[idx]:
			__lowercase							,    __lowercase   							=	self.heap[idx], self.heap[p]
			__lowercase							,    __lowercase   							=	(
			    self.idx_of_element[self.heap[idx]],
			    self.idx_of_element[self.heap[p]],
			)
			__lowercase   							=	p
			__lowercase   							=	self.get_parent_idx(_lowerCAmelCase      )







	def 			_a (	self   :				Optional[int]      )		->			int:
		"""simple docstring"""







		return self.heap[0]







	def 			_a (	self   :				str      )		->			str:
		"""simple docstring"""







		__lowercase							,    __lowercase   							=	self.heap[-1], self.heap[0]
		__lowercase							,    __lowercase   							=	(
		    self.idx_of_element[self.heap[-1]],
		    self.idx_of_element[self.heap[0]],
		)

		__lowercase   							=	self.heap.pop()
		del self.idx_of_element[x]
		self.sift_down(0							,					self.heap      )
		return x







	def 			_a (	self   :				Union[str, Any]							,					_lowerCAmelCase   :				List[str]      )		->			Optional[int]:
		"""simple docstring"""







		self.heap.append(_lowerCAmelCase      )
		__lowercase   							=	len(self.heap      ) - 1
		__lowercase   							=	node.val
		self.sift_up(len(self.heap      ) - 1      )







	def 			_a (	self   :				List[Any]      )		->			int:
		"""simple docstring"""







		return len(self.heap      ) == 0







	def 			_a (	self   :				Tuple							,					_lowerCAmelCase   :				List[str]							,					_lowerCAmelCase   :				Union[str, Any]      )		->			int:
		"""simple docstring"""







		assert (
		    self.heap[self.idx_of_element[node]].val > new_value
		), "newValue must be less that current value"
		__lowercase   							=	new_value
		__lowercase   							=	new_value
		self.sift_up(self.idx_of_element[node]      )

__UpperCamelCase   :   Tuple      =    Node("""R""", -1)
__UpperCamelCase   :   Union[str, Any]      =    Node("""B""", 6)
__UpperCamelCase   :   Optional[Any]      =    Node("""A""", 3)
__UpperCamelCase   :   Union[str, Any]      =    Node("""X""", 1)
__UpperCamelCase   :   Any      =    Node("""E""", 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
__UpperCamelCase   :   Tuple      =    MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print("""Min Heap - before decrease key""")
for i in my_min_heap.heap:
						print(i)

print("""Min Heap - After decrease key of node [B -> -17]""")
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
						print(i)

if __name__ == "__main__":
						import doctest

						doctest.testmod()



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase        =				{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RobertaPreLayerNormForCausalLM""",
					    """RobertaPreLayerNormForMaskedLM""",
					    """RobertaPreLayerNormForMultipleChoice""",
					    """RobertaPreLayerNormForQuestionAnswering""",
					    """RobertaPreLayerNormForSequenceClassification""",
					    """RobertaPreLayerNormForTokenClassification""",
					    """RobertaPreLayerNormModel""",
					    """RobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRobertaPreLayerNormForCausalLM""",
					    """TFRobertaPreLayerNormForMaskedLM""",
					    """TFRobertaPreLayerNormForMultipleChoice""",
					    """TFRobertaPreLayerNormForQuestionAnswering""",
					    """TFRobertaPreLayerNormForSequenceClassification""",
					    """TFRobertaPreLayerNormForTokenClassification""",
					    """TFRobertaPreLayerNormMainLayer""",
					    """TFRobertaPreLayerNormModel""",
					    """TFRobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """FlaxRobertaPreLayerNormForCausalLM""",
					    """FlaxRobertaPreLayerNormForMaskedLM""",
					    """FlaxRobertaPreLayerNormForMultipleChoice""",
					    """FlaxRobertaPreLayerNormForQuestionAnswering""",
					    """FlaxRobertaPreLayerNormForSequenceClassification""",
					    """FlaxRobertaPreLayerNormForTokenClassification""",
					    """FlaxRobertaPreLayerNormModel""",
					    """FlaxRobertaPreLayerNormPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roberta_prelayernorm import (
					    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    RobertaPreLayerNormConfig,
					    RobertaPreLayerNormOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta_prelayernorm import (
										    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaPreLayerNormForCausalLM,
										    RobertaPreLayerNormForMaskedLM,
										    RobertaPreLayerNormForMultipleChoice,
										    RobertaPreLayerNormForQuestionAnswering,
										    RobertaPreLayerNormForSequenceClassification,
										    RobertaPreLayerNormForTokenClassification,
										    RobertaPreLayerNormModel,
										    RobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta_prelayernorm import (
										    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaPreLayerNormForCausalLM,
										    TFRobertaPreLayerNormForMaskedLM,
										    TFRobertaPreLayerNormForMultipleChoice,
										    TFRobertaPreLayerNormForQuestionAnswering,
										    TFRobertaPreLayerNormForSequenceClassification,
										    TFRobertaPreLayerNormForTokenClassification,
										    TFRobertaPreLayerNormMainLayer,
										    TFRobertaPreLayerNormModel,
										    TFRobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta_prelayernorm import (
										    FlaxRobertaPreLayerNormForCausalLM,
										    FlaxRobertaPreLayerNormForMaskedLM,
										    FlaxRobertaPreLayerNormForMultipleChoice,
										    FlaxRobertaPreLayerNormForQuestionAnswering,
										    FlaxRobertaPreLayerNormForSequenceClassification,
										    FlaxRobertaPreLayerNormForTokenClassification,
										    FlaxRobertaPreLayerNormModel,
										    FlaxRobertaPreLayerNormPreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase        =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


